The soil-transmitted helminthic infections are major international health concerns, affecting over a quarter of the world's population. Despite the availability of effective drugs, geohelminths persist as major causes of morbidity in the developing world, indicating the critical need for continued research on the determinants of susceptibility to this important class of infections. Work conducted by this project during the last five years has demonstrated that there are significant genetic components to susceptibility to helminthic infections. Using data generated for 2000 members of a single Nepalese pedigree, we have localized major quantitative trait loci effects on susceptibility to roundworm (Ascaris lumbricoides), hookworm (Necatur americanus and Ancylostoma duodenale), and whipworm (Trichuris trichiura) infection to a total of 10 chromosomal regions. In the proposed next phase of the study, we will test hypotheses about the specific genes responsible for these effects as part of the first study ever focused on the identification of genes responsible for differential susceptibility to a parasitic infection. The overall goal of the research is to identify the specific genes responsible for the QTL effects and characterize the functional variants within those genes. We will refine the localization of QTLs influencing susceptibility to helminthic infection by fine mapping of QTL regions with additional STR markers. We will then use gene expression assays to prioritize positional candidate genes for differential susceptibility to helminthic infection located under the narrowed linkage peak. Association analyses of SNPs placed within high priority candidates for genes determining differential susceptibility to helminthic infection will be used to determine which candidates are responsible for the QTL effects. We anticipate identifying 6 genes, which we will resequence in 50 people to identify all common polymorphisms in the genes and use quantitative trait nucleotide analysis to identify the functional variants. Subsequently we will type the functional variants in larger data sets to determine which variants account for the QTL effect.